import numpy as np

def smooth_curve(x):

    window_len = 11
    s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]
    w = np.kaiser(window_len, 2)
    y = np.convolve(w / w.sum(), s, mode='valid')
    return y[5:len(y) - 5]

def shuffle_dataset(x, t):
    permutation = np.random.permutation(x.shape[0])
    x = x[permutation, :] if x.ndim == 2 else x[permutation,:,:,:]
    t = t[permutation]
    return x, t

def conv_output_size(input_size, filter_size, stride=1, pad=0):
    return(input_size + 2 * pad - filter_size) / stride + 1

def im2col(input_data, filter_h, filter_w, stride=1, pad=0):
    N, C, H, W = input_data.shape
    out_h = (H + 2 * pad - filter_h) // stride + 1
    out_w = (W + 2 * pad - filter_w) // stride + 1

    img = np.pad(input_data, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
    col = np.zeros((N, C, filter_h, filter_w, out_h, out_w))

    for y in range(filter_h):
        y_max = y + stride * out_h
        for x in range(filter_w):
            x_max = x + stride * out_w
            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
    col = col.transpose(0, 4, 5, 1, 2, 3).reshape(N * out_h * out_w, -1)
    return col

def col2im(col, input_shape, filter_h, filter_w, stride = 1, pad = 0):
    N, C, H, W = input_shape
    out_h = (H + 2 * pad - filter_h) // stride + 1
    out_w = (W + 2 * pad - filter_w) // stride + 1
    col = col.reshape(N, out_h, out_w, C, filter_h, filter_w).transpose(0, 3, 4, 5, 1, 2)

    img = np.zeros((N, C, H + 2 * pad + stride - 1, W + 2 * pad + stride - 1))
    for y in range(filter_h):
        y_max = y + stride * out_h
        for x in range(filter_w):
            x_max = x + stride * out_w
            img[:, :, y:y_max:stride, x:x_max:stride] += col[:, :, y, x, :, :]

    return img[:, :, pad:H + pad, pad:W + pad]